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Abstract
Protein–protein interaction (PPI) prediction is vital for interpreting biological activities. Even though many diverse sorts of data and machine learning approaches have been employed in PPI prediction, performance still has to be enhanced. As a result, we adopted an Aquilla Influenced Shark Smell (AISSO)-based hybrid prediction technique to construct a sequence-dependent PPI prediction model. This model has two stages of operation: feature extraction and prediction. Along with sequence-based and Gene Ontology features, unique features were produced in the feature extraction stage utilizing the improved semantic similarity technique, which may deliver reliable findings. These collected characteristics were then sent to the prediction step, and hybrid neural networks, such as the Improved Recurrent Neural Network and Deep Belief Networks, were used to predict the PPI using modified score level fusion. These neural networks’ weight variables were adjusted utilizing a unique optimal methodology called Aquila Influenced Shark Smell (AISSO), and the outcomes showed that the developed model had attained an accuracy of around 88%, which is much better than the traditional methods; this model AISSO-based PPI prediction can provide precise and effective predictions.
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1 DCSA, Maharshi Dayanand University, Rohtak, India (GRID:grid.411524.7) (ISNI:0000 0004 1790 2262)
2 Arba Minch University, Arba Minch, Ethiopia (GRID:grid.442844.a) (ISNI:0000 0000 9126 7261); Galgotias University, Department of Computer Science and Engineering, Greater Noida, India (GRID:grid.448824.6) (ISNI:0000 0004 1786 549X)
3 Galgotias University, Department of Computer Science and Engineering, Greater Noida, India (GRID:grid.448824.6) (ISNI:0000 0004 1786 549X)
4 Taif University, Department of Computer Science, College of Computers and Information Technology, Taif, Saudi Arabia (GRID:grid.412895.3) (ISNI:0000 0004 0419 5255)
5 Taif University, Department of Information Technology, College of Computers and Information Technology, Taif, Saudi Arabia (GRID:grid.412895.3) (ISNI:0000 0004 0419 5255)
6 King Abdulaziz University, Department of Information Systems, Faculty of Computing and Information Technology, Jeddah, Saudi Arabia (GRID:grid.412125.1) (ISNI:0000 0001 0619 1117)